Truncation of the constant domain drives amyloid formation by immunoglobulin light chains.

AL amyloidosis is a life-threatening disease caused by deposition of immunoglobulin light chains. While the mechanisms underlying light chains amyloidogenesis in vivo remain unclear, several studies have highlighted the role that tissue environment and structural amyloidogenicity of individual light chains have in the disease pathogenesis. AL natural deposits contain both full-length light chains and fragments encompassing the variable domain (VL) as well as different length segments of the constant region (CL), thus highlighting the relevance that proteolysis may have in the fibrillogenesis pathway. Here, we investigate the role of major truncated species of the disease-associated AL55 light chain that were previously identified in natural deposits. Specifically, we study structure, molecular dynamics, thermal stability, and capacity to form fibrils of a fragment containing both the VL and part of the CL (133-AL55), in comparison with the full-length protein and its variable domain alone, under shear stress and physiological conditions. Whereas the full-length light chain forms exclusively amorphous aggregates, both fragments generate fibrils, although, with different kinetics, aggregate structure, and interplay with the unfragmented protein. More specifically, the VL-CL 133-AL55 fragment entirely converts into amyloid fibrils microscopically and spectroscopically similar to their ex vivo counterpart and increases the amorphous aggregation of full-length AL55. Overall, our data support the idea that light chain structure and proteolysis are both relevant for amyloidogenesis in vivo and provide a novel biocompatible model of light chain fibrillogenesis suitable for future mechanistic studies.

Can we predict pathology without surgery? Weighing the added value of multiparametric MRI and whole prostate radiomics in integrative machine learning models.

OBJECTIVE: To test the ability of high-performance machine learning (ML) models employing clinical, radiological, and radiomic variables to improve non-invasive prediction of the pathological status of prostate cancer (PCa) in a large, single-institution cohort. METHODS: Patients who underwent multiparametric MRI and prostatectomy in our institution in 2015-2018 were considered; a total of 949 patients were included. Gradient-boosted decision tree models were separately trained using clinical features alone and in combination with radiological reporting and/or prostate radiomic features to predict pathological T, pathological N, ISUP score, and their change from preclinical assessment. Model behavior was analyzed in terms of performance, feature importance, Shapley additive explanation (SHAP) values, and mean absolute error (MAE). The best model was compared against a naïve model mimicking clinical workflow. RESULTS: The model including all variables was the best performing (AUC values ranging from 0.73 to 0.96 for the six endpoints). Radiomic features brought a small yet measurable boost in performance, with the SHAP values indicating that their contribution can be critical to successful prediction of endpoints for individual patients. MAEs were lower for low-risk patients, suggesting that the models find them easier to classify. The best model outperformed (p ≤ 0.0001) clinical baseline, resulting in significantly fewer false negative predictions and overall was less prone to under-staging. CONCLUSIONS: Our results highlight the potential benefit of integrative ML models for pathological status prediction in PCa. Additional studies regarding clinical integration of such models can provide valuable information for personalizing therapy offering a tool to improve non-invasive prediction of pathological status. CLINICAL RELEVANCE STATEMENT: The best machine learning model was less prone to under-staging of the disease. The improved accuracy of our pathological prediction models could constitute an asset to the clinical workflow by providing clinicians with accurate pathological predictions prior to treatment. KEY POINTS: • Currently, the most common strategies for pre-surgical stratification of prostate cancer (PCa) patients have shown to have suboptimal performances. • The addition of radiological features to the clinical features gave a considerable boost in model performance. Our best model outperforms the naïve model, avoiding under-staging and resulting in a critical advantage in the clinic. •Machine learning models incorporating clinical, radiological, and radiomics features significantly improved accuracy of pathological prediction in prostate cancer, possibly constituting an asset to the clinical workflow.

Multiparametric MRI-based 5-year Risk Prediction Model for Biochemical Recurrence of Prostate Cancer after Radical Prostatectomy.

Background Current predictive tools to estimate the risk of biochemical recurrence (BCR) after treatment of prostate cancer do not consider multiparametric MRI (mpMRI) information. Purpose To develop a risk prediction tool that considers mpMRI findings to assess the risk of 5-year BCR after radical prostatectomy. Materials and Methods In this retrospective single-center analysis in 1459 patients with prostate cancer who underwent mpMRI before radical prostatectomy (in 2012-2015), the outcome of interest was 5-year BCR (two consecutive prostate-specific antigen [PSA] levels > 0.2 ng/mL [0.2 µg/L]). Patients were randomly divided into training (70%) and test (30%) sets. Kaplan-Meier plots were applied to the training set to estimate survival probabilities. Multivariable Cox regression models were used to test the relationship between BCR and different sets of exploratory variables. The C-index of the final model was calculated for the training and test sets and was compared with European Association of Urology, University of California San Francisco Cancer of the Prostate Risk Assessment, Memorial Sloan-Kettering Cancer Center, and Partin risk tools using the partial likelihood ratio test. Five risk categories were created. Results The median duration of follow-up in the whole cohort was 59 months (IQR, 32-81 months); 376 of 1459 (25.8%) patients had BCR. A multivariable Cox regression model (referred to as PIPEN, and composed of PSA density, International Society of Urological Pathology grade group, Prostate Imaging Reporting and Data System category, European Society of Urogenital Radiology extraprostatic extension score, nodes) fitted to the training data yielded a C-index of 0.74, superior to that of other predictive tools (C-index 0.70 for all models; P ≤ .01) and a median higher C-index on 500 test set replications (C-index, 0.73). Five PIPEN risk categories were identified with 5-year BCR-free survival rates of 92%, 84%, 71%, 56%, and 26% in very low-, low-, intermediate-, high-, and very high-risk patients, respectively (all P < .001). Conclusion A five-item model for predicting the risk of 5-year BCR after radical prostatectomy for prostate cancer was developed and internally verified, and five risk categories were identified. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Aguirre and Ortegón in this issue.

[18 F]-FDG PET radiomic model as prognostic biomarker in diffuse large B-cell lymphoma.

To evaluate the association between radiomic features (RFs) extracted from 18 F-FDG PET/CT (18 F-FDG-PET) with progression-free survival (PFS) and overall survival (OS) in diffuse large-B-cell lymphoma (DLBCL) patients eligible to first-line chemotherapy. DLBCL patients who underwent 18 F-FDG-PET prior to first-line chemotherapy were retrospectively analyzed. RFs were extracted from the lesion showing the highest uptake. A radiomic score to predict PFS and OS was obtained by multivariable Elastic Net Cox model. Radiomic univariate model, clinical and combined clinical-radiomic multivariable models to predict PFS and OS were obtained. 112 patients were analyzed. Median follow-up was 34.7 months (Inter-Quartile Range (IQR) 11.3-66.3 months) for PFS and 41.1 (IQR 18.4-68.9) for OS. Radiomic score resulted associated with PFS and OS (p < 0.001), outperforming conventional PET parameters. C-index (95% CI) for PFS prediction were 0.67 (0.58-0.76), 0.81 (0.75-0.88) and 0.84 (0.77-0.91) for clinical, radiomic and combined clinical-radiomic model, respectively. C-index for OS were 0.77 (0.66-0.89), 0.84 (0.76-0.91) and 0.90 (0.81-0.98). In the Kaplan-Meier analysis (low-IPI vs. high-IPI), the radiomic score was significant predictor of PFS (p < 0.001). The radiomic score was an independent prognostic biomarker of survival in DLBCL patients. The extraction of RFs from baseline 18 F-FDG-PET might be proposed in DLBCL to stratify high-risk versus low-risk patients of relapse after first-line therapy, especially in low-IPI patients.

European Cohorts of patients and schools to Advance Response to Epidemics (EuCARE): a cluster randomised interventional and observational study protocol to investigate the relationship between schools and SARS-CoV-2 infection.

BACKGROUND: Contradictory results were reported on the role of school closure/reopening on the overall SARS-CoV-2 transmission rate, as well as on which kind and level of mitigation measures implemented in schools may be effective in limiting its diffusion. Some recent studies were reassuring, showing that opening did not increase the community spread, although teachers and families are worried about the high class density. On the other hand, distance learning was associated with a negative impact on learning, sociability and psychological health, especially in vulnerable children. As it becomes clear that the SARS-CoV-2 pandemic will last for a long time, there is a high need for studies and solutions to support safe schools opening based on scientific evidence of harms and benefits. The Lolli-Methode (LM) is a strategy for epidemiological surveillance and early intervention aiming at SARS-CoV-2 outbreaks' reduction in schools, relying on polymerase chain reaction analysis of saliva samples. METHODS: In this cluster randomised trial protocol, we aim to determine whether the LM is useful to support schools opening and to reduce clusters and attack rates in schools, compared with the standard of care (SoC) surveillance by public health departments. This multicenter study will enrol 440 classes (around 8800 students, teachers and other personnel) from two countries, cluster randomised to LM or SoC. The samples from the pools will be collected and tested using PCR-based techniques. Test results will be combined with questionnaires filled in by children, parents, schoolteachers, and principals, concerning ongoing mitigation measures, their perceived psychological impact and other health and socio-economic information. An ancillary observational study will be carried out to study the prevalence of SARS-CoV-2 in schools, frequencies and size of clusters and attack rates, to compare the effectiveness of the different preventive measures adopted and to evaluate psychological issues in students and teachers in relation to the pandemic's containment measures. DISCUSSION: By the end of this study, we will have defined and characterised the applicability of the LM for SARS-CoV-2 surveillance, as well as the impact of pandemic preventive measures on children and teachers. Trial registration International Standard Randomised Controlled Trial Number: NCT05396040, 27.05.2022.

Comparison of automated segmentation techniques for magnetic resonance images of the prostate.

BACKGROUND: Contouring of anatomical regions is a crucial step in the medical workflow and is both time-consuming and prone to intra- and inter-observer variability. This study compares different strategies for automatic segmentation of the prostate in T2-weighted MRIs. METHODS: This study included 100 patients diagnosed with prostate adenocarcinoma who had undergone multi-parametric MRI and prostatectomy. From the T2-weighted MR images, ground truth segmentation masks were established by consensus from two expert radiologists. The prostate was then automatically contoured with six different methods: (1) a multi-atlas algorithm, (2) a proprietary algorithm in the Syngo.Via medical imaging software, and four deep learning models: (3) a V-net trained from scratch, (4) a pre-trained 2D U-net, (5) a GAN extension of the 2D U-net, and (6) a segmentation-adapted EfficientDet architecture. The resulting segmentations were compared and scored against the ground truth masks with one 70/30 and one 50/50 train/test data split. We also analyzed the association between segmentation performance and clinical variables. RESULTS: The best performing method was the adapted EfficientDet (model 6), achieving a mean Dice coefficient of 0.914, a mean absolute volume difference of 5.9%, a mean surface distance (MSD) of 1.93 pixels, and a mean 95th percentile Hausdorff distance of 3.77 pixels. The deep learning models were less prone to serious errors (0.854 minimum Dice and 4.02 maximum MSD), and no significant relationship was found between segmentation performance and clinical variables. CONCLUSIONS: Deep learning-based segmentation techniques can consistently achieve Dice coefficients of 0.9 or above with as few as 50 training patients, regardless of architectural archetype. The atlas-based and Syngo.via methods found in commercial clinical software performed significantly worse (0.855[Formula: see text]0.887 Dice).

Post-diagnosis smoking cessation and survival of patients with head and neck cancer: a systematic review and meta-analysis.

Cigarette smoking is the main risk factor for head and neck cancer (HNC) and many HNC patients are active smokers at diagnosis. We conducted a systematic literature review and meta-analysis to quantify the survival impact of smoking cessation at or around the time of HNC diagnosis. We searched studies published until December 31, 2021, and used random-effects meta-analysis to pool study-specific estimates into summary hazard ratio (SHR) and corresponding 95% confidence intervals (CI). Sixteen studies were published between 1983 and 2021, and over 2300 HNC patients were included. Studies were diverse in terms of design, patients, tumours and treatment characteristics, and criteria used to discriminate quitters from continued smokers. HNC patients who quit smoking at or around diagnosis had significantly better overall survival than continued smokers (SHR 0.80, 95% CI 0.70-0.91, n studies = 10). A beneficial effect of post-diagnosis smoking cessation was suggested for other survival endpoints as well, but the results were based on fewer studies (n = 5) and affected by publication bias. Cessation counselling should be offered to all smokers who start a diagnostic workup for HNC and should be considered standard multidisciplinary oncological care for HNC patients. PROSPERO registration number CRD42021245560.

Radiomic Features Applied to Contrast Enhancement Spectral Mammography: Possibility to Predict Breast Cancer Molecular Subtypes in a Non-Invasive Manner.

We aimed to investigate the association between the radiomic features of contrast-enhanced spectral mammography (CESM) images and a specific receptor pattern of breast neoplasms. In this single-center retrospective study, we selected patients with neoplastic breast lesions who underwent CESM before a biopsy and surgical assessment between January 2013 and February 2022. Radiomic analysis was performed on regions of interest selected from recombined CESM images. The association between the features and each evaluated endpoint (ER, PR, Ki-67, HER2+, triple negative, G2-G3 expressions) was investigated through univariate logistic regression. Among the significant and highly correlated radiomic features, we selected only the one most associated with the endpoint. From a group of 321 patients, we enrolled 205 malignant breast lesions. The median age at the exam was 50 years (interquartile range (IQR) 45-58). NGLDM_Contrast was the only feature that was positively associated with both ER and PR expression (p-values = 0.01). NGLDM_Coarseness was negatively associated with Ki-67 expression (p-value = 0.02). Five features SHAPE Volume(mL), SHAPE_Volume(vx), GLRLM_RLNU, NGLDM_Busyness and GLZLM_GLNU were all positively and significantly associated with HER2+; however, all of them were highly correlated. Radiomic features of CESM images could be helpful to predict particular molecular subtypes before a biopsy.

Comparative study of the stabilities of synthetic in vitro and natural ex vivo transthyretin amyloid fibrils.

Systemic amyloidosis caused by extracellular deposition of insoluble fibrils derived from the pathological aggregation of circulating proteins, such as transthyretin, is a severe and usually fatal condition. Elucidation of the molecular pathogenic mechanism of the disease and discovery of effective therapies still represents a challenging medical issue. The in vitro preparation of amyloid fibrils that exhibit structural and biochemical properties closely similar to those of natural fibrils is central to improving our understanding of the biophysical basis of amyloid formation in vivo and may offer an important tool for drug discovery. Here, we compared the morphology and thermodynamic stability of natural transthyretin fibrils with those of fibrils generated in vitro either using the common acidification procedure or primed by limited selective cleavage by plasmin. The free energies for fibril formation were -12.36, -8.10, and -10.61 kcal mol-1, respectively. The fibrils generated via plasmin cleavage were more stable than those prepared at low pH and were thermodynamically and morphologically similar to natural fibrils extracted from human amyloidotic tissue. Determination of thermodynamic stability is an important tool that is complementary to other methods of structural comparison between ex vivo fibrils and fibrils generated in vitro Our finding that fibrils created via an in vitro amyloidogenic pathway are structurally similar to ex vivo human amyloid fibrils does not necessarily establish that the fibrillogenic pathway is the same for both, but it narrows the current knowledge gap between in vitro models and in vivo pathophysiology.

Germline MC1R variants and frequency of somatic BRAF, NRAS, and TERT mutations in melanoma: Literature review and meta-analysis.

Germline variants of the melanocortin-1-receptor (MC1R) gene are the most common genetic trait predisposing to cutaneous melanoma (CM). Here, we performed a literature review and meta-analysis of the association between MC1R gene variants and the frequency of somatic mutations of the BRAF, NRAS, and TERT genes in CM patients. We included studies published until January 2020 in MEDLINE, EMBASE, Ovid Medline, and two grey literature databases. Random effect models were used to pool study-specific estimates into summary odds ratio (SOR) and 95% confidence intervals (CIs). Subgroup and sensitivity analyses were conducted to identify potential sources of heterogeneity and assess the robustness of pooled estimates. Twelve studies published between 2006 and 2018 (encompassing 3566 CM, mostly on nonacral sites) were included. MC1R gene variants were not significantly associated with the frequency of somatic mutations of the BRAF and NRAS genes. Only three studies focused on somatic mutations of the TERT gene promoter, all of which reported moderate-to-strong positive associations with MC1R germline variants. MC1R gene variants appear to make only moderate changes, if any, to the risk of BRAF- or NRAS-mutant CM. The association with TERT promoter mutations is suggestive, yet it warrants confirmation as it is based on a still limited number of studies.

MRI-based radiomics signature for localized prostate cancer: a new clinical tool for cancer aggressiveness prediction? Sub-study of prospective phase II trial on ultra-hypofractionated radiotherapy (AIRC IG-13218).

OBJECTIVES: Radiomic involves testing the associations of a large number of quantitative imaging features with clinical characteristics. Our aim was to extract a radiomic signature from axial T2-weighted (T2-W) magnetic resonance imaging (MRI) of the whole prostate able to predict oncological and radiological scores in prostate cancer (PCa). METHODS: This study included 65 patients with localized PCa treated with radiotherapy (RT) between 2014 and 2018. For each patient, the T2-W MRI images were normalized with the histogram intensity scale standardization method. Features were extracted with the IBEX software. The association of each radiomic feature with risk class, T-stage, Gleason score (GS), extracapsular extension (ECE) score, and Prostate Imaging Reporting and Data System (PI-RADS v2) score was assessed by univariate and multivariate analysis. RESULTS: Forty-nine out of 65 patients were eligible. Among the 1702 features extracted, 3 to 6 features with the highest predictive power were selected for each outcome. This analysis showed that texture features were the most predictive for GS, PI-RADS v2 score, and risk class; intensity features were highly associated with T-stage, ECE score, and risk class, with areas under the receiver operating characteristic curve (ROC AUC) ranging from 0.74 to 0.94. CONCLUSIONS: MRI-based radiomics is a promising tool for prediction of PCa characteristics. Although a significant association was found between the selected features and all the mentioned clinical/radiological scores, further validations on larger cohorts are needed before these findings can be applied in the clinical practice. KEY POINTS: • A radiomic model was used to classify PCa aggressiveness. • Radiomic analysis was performed on T2-W magnetic resonance images of the whole prostate gland. • The most predictive features belong to the texture (57%) and intensity (43%) domains.

Melanoma Epidemiology and Sun Exposure.

The worldwide incidence of melanoma has increased rapidly over the last 50 years. Melanoma is the most common cancer found in the young adult population, and its incidence is very high among geriatric populations. The incidence of melanoma varies by sex, and this factor is also associated with differences in the anatomical site melanoma. Adolescent and young adult women have a higher incidence than men. This may be, in part, due to the greater use of sunbeds, as well as intentional sun exposure among girls and, in general, risky behaviours in seeking to suntan, due to socially-determined aesthetic needs. Indeed, the World Health Organization declared that there is sufficient evidence to classify exposure to ultraviolet radiation (sunbed use and sun exposure) as carcinogenic to humans. Although pigmentation characteristics, such as skin colour, hair and eye colour, freckles and number of common and atypical naevi, do influence susceptibility to melanoma, recommendations regarding prevention should be directed to the entire population and should include avoiding sunbed, covering sun-exposed skin, wearing a hat and sunglasses. Sunscreen use should not be used to prolong intentional sun exposure. Primary prevention should be focused mainly on young adult women, while secondary prevention should be focused mainly on elderly men. In fact, after the age of 40 years, incidence rates reverse, and the incidence of melanoma among men is greater than among women. This is probably due to the fact that men are less likely than women to examine their own skin or present to a dermatologist for skin examination.

Vitamin D Receptor Polymorphisms and Cancer.

Increasing scientific evidence supports the link between vitamin D and cancer risk. The active metabolite 1,25(OH)2D exerts its activity by binding to the vitamin D receptor (VDR), an intracellular receptor that mediates transcriptional activation and repression of target genes. The binding of 1,25(OH)2D to VDR is able to regulate hundreds of different genes. VDR is active in virtually all tissues including the colon, breast, lung, ovary, bone, kidney, parathyroid gland, pancreatic b-cells, monocytes, T lymphocytes, melanocytes, keratinocytes, and also cancer cells.The relevance of VDR gene restriction fragment length polymorphisms for various types of cancer has been investigated by a great number of studies.We have carried out a systematic review of the literature to analyze the relevance of more VDR polymorphisms (Fok1, Bsm1, Taq1, Apa1, and Cdx2) for individual malignancies considering ethnicity as a key factor for heterogeneity.Up to December 2018, we identified 176 independent studies with data to assess the risk of breast, prostate, colorectal, skin (melanoma and non-melanoma skin cancer), lung, ovarian, kidney, bladder, gallbladder, esophageal, thyroid, head and neck, liver and pancreatic cancer, oral squamous cell carcinoma, non-Hodgkin lymphoma, multiple myeloma and sarcoma.Significant associations with VDR polymorphisms have been reported for prostate (Fok1, Bsm1, Taq1, Apa1, Cdx2), breast (Fok1, Bsm1, Taq1, Apa1, CdX2), colorectal (Fok1, Bsm1, Taq1, Apa1), and skin cancer (Fok1, Bsm1, Taq1). Very few studies reported risk estimates for the other cancer sites.Conflicting data have been reported for most malignancies, and at present, it is still not possible to make any definitive statements about the importance of the VDR genotype for cancer risk. It seems probable that other factors such as ethnicity, phenotype, 25(OH)D plasma levels, and UV radiation exposure play a role as confounding factors and introduce heterogeneity.To conclude, there is some indication that VDR polymorphisms may modulate the risk of some cancer sites and in future studies VDR genetic variation should be integrated also with assessment of vitamin D status and stratified by ethnicity.

Meta-analysis of diagnostic performance of serological tests for SARS-CoV-2 antibodies up to 25 April 2020 and public health implications.

We reviewed the diagnostic accuracy of SARS-CoV-2 serological tests. Random-effects models yielded a summary sensitivity of 82% for IgM, and 85% for IgG and total antibodies. For specificity, the pooled estimate were 98% for IgM and 99% for IgG and total antibodies. In populations with ≤ 5% of seroconverted individuals, unless the assays have perfect (i.e. 100%) specificity, the positive predictive value would be ≤ 88%. Serological tests should be used for prevalence surveys only in hard-hit areas.

Plasminogen activation triggers transthyretin amyloidogenesis in vitro.

Systemic amyloidosis is a usually fatal disease caused by extracellular accumulation of abnormal protein fibers, amyloid fibrils, derived by misfolding and aggregation of soluble globular plasma protein precursors. Both WT and genetic variants of the normal plasma protein transthyretin (TTR) form amyloid, but neither the misfolding leading to fibrillogenesis nor the anatomical localization of TTR amyloid deposition are understood. We have previously shown that, under physiological conditions, trypsin cleaves human TTR in a mechano-enzymatic mechanism that generates abundant amyloid fibrils in vitro In sharp contrast, the widely used in vitro model of denaturation and aggregation of TTR by prolonged exposure to pH 4.0 yields almost no clearly defined amyloid fibrils. However, the exclusive duodenal location of trypsin means that this enzyme cannot contribute to systemic extracellular TTR amyloid deposition in vivo Here, we therefore conducted a bioinformatics search for systemically active tryptic proteases with appropriate tissue distribution, which unexpectedly identified plasmin as the leading candidate. We confirmed that plasmin, just as trypsin, selectively cleaves human TTR between residues 48 and 49 under physiological conditions in vitro Truncated and full-length protomers are then released from the native homotetramer and rapidly aggregate into abundant fibrils indistinguishable from ex vivo TTR amyloid. Our findings suggest that physiological fibrinolysis is likely to play a critical role in TTR amyloid formation in vivo Identification of this surprising intersection between two hitherto unrelated pathways opens new avenues for elucidating the mechanisms of TTR amyloidosis, for seeking susceptibility risk factors, and for therapeutic innovation.

Inter-observer agreement on the morphology of screening-detected lung cancer: beyond pulmonary nodules and masses.

OBJECTIVES: Pulmonary nodules and masses are the typical presentations of lung cancer. However, a spectrum of focal opacities cannot be defined as either "pulmonary nodule" or "mass," despite representing cancer. We aimed to assess the morphology of screening-detected lung cancers at low-dose computed tomography LDTC and to evaluate inter-observer agreement in their classification. METHODS: Four radiologists with different experiences in thoracic imaging retrospectively reviewed 273 screening-detected lung cancers. Readers were asked to assess if morphology at the time of diagnosis was consistent with the Fleischner Society definition of pulmonary "nodule" or "mass." Cancers not consistent were defined as "non-nodular/non-mass" (NN/NM) and sub-classified as follows: associated with cystic airspaces, stripe-like, scar-like, endobronchial, or not otherwise defined (NOD). Inter-observer agreement was evaluated using Cohen's K statistic among pairs of readers and modified Fleiss' kappa statistic for overall agreement. RESULTS: Two hundred forty-one of the 273 (88%) lesions were defined as pulmonary nodule or mass by complete agreement, while 20/273 (7.3%) were defined as NN/NM. Six (2.2%) of 273 were sub-classified as lesions associated with cystic airspace, six (2.2%) as scar-like, five (1.8%) as endobronchial, and one (0.7%) as NOD by complete agreement. The concordance in defining morphology was excellent (261/273; 96%, 95%CI 92-98%; k 0.85, 95%CI 0.75-0.92) and also in the sub-classification (18/20; 90%, 95%CI 68-99%, k 0.93, 95%CI 0.86-1.00). There was incomplete agreement regarding lesion morphology in 4.4% (12/273) of cases. CONCLUSIONS: A non-negligible percentage of screening-detected lung cancers has a NN/NM appearance at LDCT. The concordance in defining lesion morphology was excellent. The awareness of various presentations can avoid missed or delayed diagnosis. KEY POINTS: • A non-negligible percentage of screening-detected lung cancers have neither nodular nor mass appearance at low-dose CT. • The awareness of various LDCT presentations of lung cancer can avoid missed or delayed diagnosis. • Optimal protocol management in CT screening should take into consideration lung nodules as well as various other focal abnormalities.

Individual fatty acids in erythrocyte membranes are associated with several features of the metabolic syndrome in obese children.

PURPOSE: Obesity leads to the clustering of cardiovascular (CV) risk factors and the metabolic syndrome (MetS) also in children and is often accompanied by non-alcoholic fatty liver disease. Quality of dietary fat, beyond the quantity, can influence CV risk profile and, in particular, omega-3 fatty acids (FA) have been proposed as beneficial in this setting. The aim of the study was to evaluate the associations of individual CV risk factors, characterizing the MetS, with erythrocyte membrane FA, markers of average intake, in a group of 70 overweight/obese children. METHODS: We conducted an observational study. Erythrocyte membrane FA were measured by gas chromatography. Spearman correlation coefficients (rS) were calculated to evaluate associations between FA and features of the MetS. RESULTS: Mean content of Omega-3 FA was low (Omega-3 Index = 4.7 ± 0.8%). Not omega-3 FA but some omega-6 FA, especially arachidonic acid (AA), were inversely associated with several features of the MetS: AA resulted inversely correlated with waist circumference (rS = - 0.352), triglycerides (rS = - 0.379), fasting insulin (rS = - 0.337) and 24-h SBP (rS = - 0.313). Total amount of saturated FA (SFA) and specifically palmitic acid, correlated positively with waist circumference (rS = 0.354), triglycerides (rS = 0.400) and fasting insulin (rS = 0.287). Fatty Liver Index (FLI), a predictive score of steatosis based on GGT, triglycerides and anthropometric indexes, was positively correlated to palmitic acid (rS = 0.515) and inversely to AA (rS = - 0.472). CONCLUSIONS: Our data suggest that omega-6 FA, and especially AA, could be protective toward CV risk factors featuring the MetS and also to indexes of hepatic steatosis in obese children, whereas SFA seems to exert opposite effects.

Oleuropein aglycone: A polyphenol with different targets against amyloid toxicity.

BACKGROUND: Many data highlight the benefits of the Mediterranean diet and its main lipid component, extra-virgin olive oil (EVOO). EVOO contains many phenolic compounds that have been found effective against several aging- and lifestyle-related diseases, including neurodegeneration. Oleuropein, a phenolic secoiroid glycoside, is the main polyphenol in the olive oil. It has been reported that the aglycone form of Oleuropein (OleA) interferes in vitro and in vivo with amyloid aggregation of a number of proteins/peptides involved in amyloid, particularly neurodegenerative, diseases avoiding the growth of toxic oligomers and displaying protection against cognitive deterioration. METHODS: In this study, we carried out a cellular and biophysical study on the relationships between the effects of OleA on the aggregation and cell interactions of the D76N ß2-microglobulin (D76N b2m) variant associated with a familial form of systemic amyloidosis with progressive bowel dysfunction and extensive visceral amyloid deposits. RESULTS: Our results indicate that OleA protection against D76N b2m cytotoxicity results from i) a modification of the conformational and biophysical properties of its amyloid fibrils; ii) a modification of the cell bilayer surface properties of exposed cells. CONCLUSIONS: This study reveals that OleA remodels not only D76N b2m aggregates but also the cell membrane interfering with the misfolded proteins-cell membrane association, in most cases an early event triggering amyloid-mediated cytotoxicity. GENERAL SIGNIFICANCE: The data provided in the present article focus on OleA protection, featuring this polyphenol as a promising plant molecule useful against amyloid diseases.

Radiomics of high-grade serous ovarian cancer: association between quantitative CT features, residual tumour and disease progression within 12 months.

OBJECTIVES: To determine if radiomic features, alone or combined with clinical data, are associated with residual tumour (RT) at surgery, and predict the risk of disease progression within 12 months (PD12) in ovarian cancer (OC) patients. METHODS: This retrospective study enrolled 101 patients according to the following inclusion parameters: cytoreductive surgery performed at our institution (9 May 2007-23 February 2016), assessment of BRCA mutational status, preoperative CT available. Radiomic features of the ovarian masses were extracted from 3D structures drawn on CT images. A phantom experiment was performed to assess the reproducibility of radiomic features. The final radiomic features included in the analysis (n = 516) were grouped into clusters using a hierarchical clustering procedure. The association of each cluster's representative radiomic feature with RT and PD12 was assessed by chi-square test. Multivariate analysis was performed using logistic regression models. P values < 0.05 were considered significant. RESULTS: Patients with values of F2-Shape/Compactness1 below the median, of F1- GrayLevelCooccurenceMatrix25/0-1InformationMeasureCorr2 below the median and of F1-GrayLevelCooccurenceMatrix25/-333-1InverseVariance above the median showed higher risk of RT (36%, 36% and 35%, respectively, as opposed to 18%, 18% and 18%). Patients with values of F4-GrayLevelRunLengthMatrix25/-333RunPercentage above the median, of F2 shape/Max3DDiameter below the median and F1-GrayLevelCooccurenceMatrix25/45-1InverseVariance above the median showed higher risk of PD12 (22%, 24% and 23%, respectively, as opposed to 6%, 5% and 6%). At multivariate analysis F2-Shape/Max3DDiameter remained significant (odds ratio (95% CI) = 11.86 (1.41-99.88)). To predict PD12, a clinical radiomics model performed better than a base clinical model. CONCLUSION: This study demonstrated significant associations between radiomic features and prognostic factors such as RT and PD12. KEY POINTS: • No residual tumour (RT) at surgery is the most important prognostic factor in OC. • Radiomic features related to mass size, randomness and homogeneity were associated with RT. • Progression of disease within 12 months (PD12) indicates worse prognosis in OC. • A model including clinical and radiomic features performed better than only-clinical model to predict PD12.